OpenRaiser/Intern-Atlas huggingface dataset:
Modeling an Augmented Reality Game Environment to Enhance Behavior of ADHD Patients
Venue: arXiv cs.HC 2019
Year: 2019

The paper generically models an augmented reality game-based environment to project the gamification of an online cognitive behavioral therapist that performs instant measurements for patients with a predefined Attention Deficit Hyperactivity Disorder (ADHD). ADHD is one of the most common neurodevelopmental disorders in which patients have difficulties related to inattention, hyperactivity, and impulsivity. Those patients are in need for a psychological therapy; the use of cognitive behavioral therapy as a firmly-established treatment is to help in enhancing the way they think and behave. A major limitation in traditional cognitive behavioral therapies is that therapists may face difficulty to optimize patients' neuropsychological stimulus following a specified treatment plan, i.e., therapists struggle to draw clear images when stimulating patients' mindset to a point where they should be. Other limitations recognized here include availability, accessibility and level-of-experience of the therapists. Therefore, the paper present a gamification model, we term as "AR-Therapist," in order to take advantages of augmented reality developments to engage patients in both real and virtual game-based environments. The model provides an on-time measurements of patients' progress throughout the treatment sessions which, in result, overcomes limitations observed in traditional cognitive behavioral therapies.
XDeep: An Interpretation Tool for Deep Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

XDeep is an open-source Python package developed to interpret deep models for both practitioners and researchers. Overall, XDeep takes a trained deep neural network (DNN) as the input, and generates relevant interpretations as the output with the post-hoc manner. From the functionality perspective, XDeep integrates a wide range of interpretation algorithms from the state-of-the-arts, covering different types of methodologies, and is capable of providing both local explanation and global explanation for DNN when interpreting model behaviours. With the well-documented API designed in XDeep, end-users can easily obtain the interpretations for their deep models at hand with several lines of codes, and compare the results among different algorithms. XDeep is generally compatible with Python 3, and can be installed through Python Package Index (PyPI). The source codes are available at: https://github.com/datamllab/xdeep.
Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE
Venue: arXiv cs.LG 2019
Year: 2019

The optimization of electric machines at multiple operating points is crucial for applications that require frequent changes on speeds and loads, such as the electric vehicles, to strive for the machine optimal performance across the entire driving cycle. However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification. Therefore, this paper proposes the utilization of t-distributed stochastic neighbor embedding (t-SNE) to visualize all of the optimization objectives of various electric machines design candidates with various operating conditions, which constitute a high-dimensional set of data that would lie on several different, but related, low-dimensional manifolds. Finally, two case studies of switched reluctance machines (SRM) are presented to illustrate the superiority of then t-SNE when compared to traditional visualization techniques used in electric machine optimizations.
Ternary MobileNets via Per-Layer Hybrid Filter Banks
Venue: arXiv cs.LG 2019
Year: 2019

MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like already compute-efficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4-6 bits albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision less than or equal to 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. The layer-wise hybrid filter banks essentially combine the strengths of full-precision and ternary weight filters to derive a compact, energy-efficient architecture for MobileNets. Using this proposed quantization method, we quantized a substantial portion of weight filters of MobileNets to ternary values resulting in 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets.
SoildNet: Soiling Degradation Detection in Autonomous Driving
Venue: arXiv cs.CV 2019
Year: 2019

In the field of autonomous driving, camera sensors are extremely prone to soiling because they are located outside of the car and interact with environmental sources of soiling such as rain drops, snow, dust, sand, mud and so on. This can lead to either partial or complete vision degradation. Hence detecting such decay in vision is very important for safety and overall to preserve the functionality of the "autonomous" components in autonomous driving. The contribution of this work involves: 1) Designing a Deep Convolutional Neural Network (DCNN) based baseline network, 2) Exploiting several network remodelling techniques such as employing static and dynamic group convolution, channel reordering to compress the baseline architecture and make it suitable for low power embedded systems with nearly 1 TOPS, 3) Comparing various result metrics of all interim networks dedicated for soiling degradation detection at tile level of size 64 x 64 on input resolution 1280 x 768. The compressed network, is called SoildNet (Sand, snOw, raIn/dIrt, oiL, Dust/muD) that uses only 9.72% trainable parameters of the base network and reduces the model size by more than 7 times with no loss in accuracy
3D-Map Assisted UAV Trajectory Design Under Cellular Connectivity Constraints
Venue: arXiv cs.IT 2019
Year: 2019

The enabling of safe cellular controlled unmanned aerial vehicle (UAV) beyond visual line of sight is expected to open important future opportunities in the area of transportation, goods delivery, and system monitoring. A key challenge in this area lies in the design of trajectories which, while allowing the completion of the UAV mission, can guarantee reliable cellular connectivity all along the path. Previous approaches in this domain have considered simplistic propagation model assumptions (e.g. Line of Sight based) or more advanced models but with computationally demanding optimized solutions. In this paper, we propose a novel approach for trajectory design using a coverage map that can be obtained with a combination of 3D map of the environment and radio propagation models. Leveraging on the convexity of sub-regions within the coverage map, we propose a low-complexity graph based algorithm which is shown to achieve quasi-optimal performance at a fraction of the computational cost of known optimal methods.
Fast Reliability Ranking of Matchstick Minimal Networks
Venue: arXiv cs.DM 2019
Year: 2019

In this article, we take a closer look at the reliability of large minimal networks constructed by repeated compositions of the simplest possible networks. For a given number of devices $n=2^m$ we define the set of all the possible compositions of series and parallel networks of two devices. We then define several partial orders over this set and study their properties. As far as we know the ranking problem has not been addressed before in this context, and this article establishes the first results in this direction. The usual approach when dealing with reliability of two-terminal networks is to determine existence or non-existence of uniformly most reliable networks. The problem of ranking two-terminal networks is thus more complex, but by restricting our study to the set of compositions we manage to determine and demonstrate the existence of a poset.
An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition
Venue: arXiv cs.HC 2019
Year: 2019

This paper presents a computational framework for providing affective labels to real-life situations, called A-Situ. We first define an affective situation, as a specific arrangement of affective entities relevant to emotion elicitation in a situation. Then, the affective situation is represented as a set of labels in the valence-arousal emotion space. Based on physiological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatiotemporal situation is represented as a polynomial curve called the affective curve, which bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve for reliable emotion labeling in real-world experiments, respectively concerning 1) a comparison between the results from our system and existing explicit assessments for measuring emotion, 2) physiological distinctiveness in emotional states, and 3) physiological characteristics correlated to continuous labels. The efficiency of affective curves to discriminate emotional states is evaluated through subject-dependent classification performance using bicoherence features to represent discrete affective states in the valence-arousal space. Furthermore, electroencephalography-based statistical analysis revealed the physiological correlates of the affective curves.
Compiling Arguments in an Argumentation Framework into Three-valued Logical Expressions
Venue: arXiv cs.LO 2019
Year: 2019

In this paper, we propose a new method for computing general allocators directly from completeness conditions. A general allocator is an abstraction of all complete labelings for an argumentation framework. Any complete labeling is obtained from a general allocator by assigning logical constants to variables. We proved the existence of the general allocators in our previous work. However, the construction requires us to enumerate all complete labelings for the framework, which makes the computation prohibitively slow. The method proposed in this paper enables us to compute general allocators without enumerating complete labelings. It also provides the solutions of local allocation that yield semantics for subsets of the framework. We demonstrate two applications of general allocators, stability, and a new concept for frameworks, termed arity. Moreover, the method, including local allocation, is applicable to broad extensions of frameworks, such as argumentation frameworks with set-attacks, bipolar argumentation frameworks, and abstract dialectical frameworks.
Learning to Fix Build Errors with Graph2Diff Neural Networks
Venue: arXiv cs.LG 2019
Year: 2019

Professional software developers spend a significant amount of time fixing builds, but this has received little attention as a problem in automatic program repair. We present a new deep learning architecture, called Graph2Diff, for automatically localizing and fixing build errors. We represent source code, build configuration files, and compiler diagnostic messages as a graph, and then use a Graph Neural Network model to predict a diff. A diff specifies how to modify the code's abstract syntax tree, represented in the neural network as a sequence of tokens and of pointers to code locations. Our network is an instance of a more general abstraction that we call Graph2Tocopo, which is potentially useful in any development tool for predicting source code changes. We evaluate the model on a dataset of over 500k real build errors and their resolutions from professional developers. Compared to the approach of DeepDelta (Mesbah et al., 2019), our approach tackles the harder task of predicting a more precise diff but still achieves over double the accuracy.
Superpixel-Based Background Recovery from Multiple Images
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose an intuitive method to recover background from multiple images. The implementation consists of three stages: model initialization, model update, and background output. We consider the pixels whose values change little in all input images as background seeds. Images are then segmented into superpixels with simple linear iterative clustering. When the number of pixels labelled as background in a superpixel is bigger than a predefined threshold, we label the superpixel as background to initialize the background candidate masks. Background candidate images are obtained from input raw images with the masks. Combining all candidate images, a background image is produced. The background candidate masks, candidate images, and the background image are then updated alternately until convergence. Finally, ghosting artifacts is removed with the k-nearest neighbour method. An experiment on an outdoor dataset demonstrates that the proposed algorithm can achieve promising results.
Global Regularity and Individual Variability in Dynamic Behaviors of Human Communication
Venue: arXiv cs.CY 2019
Year: 2019

A new model, called "Human Dynamics", has been recently proposed that individuals execute activities based on a perceived priority of tasks, which can be characterized by a power-law distribution of waiting time between consecutive tasks (Barabasi, 2005). This power-law distribution has been found to exist in diverse human behaviors, such as mail correspondence, e-mail communication, webpage browsing, video-on-demand, and mobile phone calls. However, the pattern has been observed at the global (i.e., aggregated) level without considering individual differences. To guard against ecological fallacy, it is necessary to test the model at the individual level. The current study aims to address the following questions: Is the power-law uniform across individuals? What distribution do individual behaviors follow? We examine these questions with a client log file of nearly 4,000 Internet users' web browsing behavior and a server log file of 2,300,000 users' file-sharing behaviors in a P2P system. The results confirm the human dynamic model at the aggregate-level both in webpage browsing and P2P usage behavior. We have also found that there is detectable variability across the individuals in the decaying rate (i.e., the exponent gamma) of the power-law distribution, which follows well-known distributions (i.e., Gaussian, Weibull, and log-normal).
Incremental extraction of a NoSQL database model using an MDA-based process
Venue: arXiv cs.IR 2019
Year: 2019

In recent years, the need to use NoSQL systems to store and exploit big data has been steadily increasing. Most of these systems are characterized by the property "schema less" which means absence of the data model when creating a database. This property brings an undeniable flexibility by allowing the evolution of the model during the exploitation of the base. However, the expression of queries requires a precise knowledge of this model. In this paper, we propose an incremental process to extract the model while operating the document-oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) that provides a formal framework for automatic model transformation. From the insert, delete and update queries executed on the database, we propose formal transformation rules with QVT to generate the physical model of the NoSQL database. An experimentation of the extraction process was performed on a medical application.
Integrated approach for the identification of spatial patterns related to renewable energy potential in European territories
Venue: arXiv cs.CY 2019
Year: 2019

The study presents an effort to classify the territories of a specific area, according to similarities in the estimated potential of their renewable sources, considering also their economic and sociodemographic structure and their geographic features. Specifically, the paper focuses on the area of EU28 and Switzerland and uses as basis for the analysis, data estimating the potential of renewable energy sources collected and elaborated in the framework of the project HotMaps (Horizon 2020). The method used to group the territorial units is cluster analysis, and specifically the k-means algorithm. The data present some interesting patterns and the territories of EU28 and Switzerland at NUTS3 level are classified into 17 clusters. The analysis shows the presence of heterogeneity within national borders and among territories comprised in the macro regions target of specific EU programmes, specifically the Adriatic-Ionian region, the Alpine region, the Baltic Sea region and the Danube region. The results of this research are meant to be used by European policy makers in developing more focused transnational renewable energy policies and strategies.
Bitcoin Coin Selection with Leverage
Venue: arXiv cs.DS 2019
Year: 2019

We present a new Bitcoin coin selection algorithm, "coin selection with leverage", which aims to improve upon cost savings than that of standard knapsack like approaches. Parameters to the new algorithm are available to be tuned at the users discretion to address other goals of coin selection. Our approach naturally fits as a replacement for the standard knapsack ingredient of full coin selection procedures.
Algorithms for Intersection Graphs of Multiple Intervals and Pseudo Disks
Venue: arXiv cs.CG 2019
Year: 2019

Intersection graphs of planar geometric objects such as intervals, disks, rectangles and pseudo-disks are well studied. Motivated by various applications, Butman et al. in SODA 2007 considered algorithmic questions in intersection graphs of $t$-intervals. A $t$-interval is a union of at most $t$ distinct intervals (here $t$ is a parameter) -- these graphs are referred to as Multiple-Interval Graphs. Subsequent work by Kammer et al. in Approx 2010 also considered $t$-disks and other geometric shapes. In this paper we revisit some of these algorithmic questions via more recent developments in computational geometry. For the minimum weight dominating set problem, we give a simple $O(t \log t)$ approximation for Multiple-Interval Graphs, improving on the previously known bound of $t^2$ . We also show that it is NP-hard to obtain an $o(t)$-approximation in this case. In fact, our results hold for the intersection graph of a set of t-pseudo-disks which is a much larger class. We obtain an $Ω(1/t)$-approximation for the maximum weight independent set in the intersection graph of $t$-pseudo-disks. Our results are based on simple reductions to existing algorithms by appropriately bounding the union complexity of the objects under consideration.
On Solving the 2-Dimensional Greedy Shooter Problem for UAVs
Venue: arXiv cs.RO 2019
Year: 2019

Unmanned Aerial Vehicles (UAVs), autonomously-guided aircraft, are widely used for tasks involving surveillance and reconnaissance. A version of the pursuit-evasion problems centered around UAVs and its variants has been extensively studied in recent years due to numerous breakthroughs in AI. We present an approach to UAV pursuit-evasion in a 2D aerial-engagement environment using reinforcement learning (RL), a machine learning paradigm concerned with goal-oriented algorithms. In this work, a UAV wielding the greedy shooter strategy engages with a UAV trained using deep Q-learning techniques. Simulated results show that the latter UAV wins every engagement in which the UAVs are suffciently separated during initialization. This approach highlights an exhaustive and robust application of reinforcement learning to pursuit-evasion that provides insight into effective strategies for UAV flight and interaction.
A Deep Learning approach for Hindi Named Entity Recognition
Venue: arXiv cs.CL 2019
Year: 2019

Named Entity Recognition is one of the most important text processing requirement in many NLP tasks. In this paper we use a deep architecture to accomplish the task of recognizing named entities in a given Hindi text sentence. Bidirectional Long Short Term Memory (BiLSTM) based techniques have been used for NER task in literature. In this paper, we first tune BiLSTM low-resource scenario to work for Hindi NER and propose two enhancements namely (a) de-noising auto-encoder (DAE) LSTM and (b) conditioning LSTM which show improvement in NER task compared to the BiLSTM approach. We use pre-trained word embedding to represent the words in the corpus, and the NER tags of the words are as defined by the used annotated corpora. Experiments have been performed to analyze the performance of different word embeddings and batch sizes which is essential for training deep models.
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework that decomposes uncertainties into epistemic and aleatoric uncertainties. We test the validity of our approach by super-resolving images of the Sun's magnetic field and by generating maps measuring the range of possible high resolution explanations compatible with a given low resolution magnetogram.
Exa-Dune -- Flexible PDE Solvers, Numerical Methods and Applications
Venue: arXiv cs.MS 2019
Year: 2019

In the Exa-Dune project we have developed, implemented and optimised numerical algorithms and software for the scalable solution of partial differential equations (PDEs) on future exascale systems exhibiting a heterogeneous massively parallel architecture. In order to cope with the increased probability of hardware failures, one aim of the project was to add flexible, application-oriented resilience capabilities into the framework. Continuous improvement of the underlying hardware-oriented numerical methods have included GPU-based sparse approximate inverses, matrix-free sum-factorisation for high-order discontinuous Galerkin discretisations as well as partially matrix-free preconditioners. On top of that, additional scalability is facilitated by exploiting massive coarse grained parallelism offered by multiscale and uncertainty quantification methods where we have focused on the adaptive choice of the coarse/fine scale and the overlap region as well as the combination of local reduced basis multiscale methods and the multilevel Monte-Carlo algorithm. Finally, some of the concepts are applied in a land-surface model including subsurface flow and surface runoff.